Single-Trial Subspace-Based Approach for VEP Extraction

Nidal S., Kamel (2010) Single-Trial Subspace-Based Approach for VEP Extraction. IEEE Transactions on Biomedical Engineering, PP (99). ISSN 0018-9294

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N. Kamel is with the A signal subspace approach for extracting visual evoked potentials (VEPs) from the background electroencephalogram (EEG) colored noise without the need for a pre-whitening stage is proposed. Linear estimation of the clean signal is performed by minimizing signal distortion while maintaining the residual noise energy below some given threshold. The generalized eigendecomposition of the covariance matrices of a VEP signal and brain background EEG noise is used to transform them jointly to diagonal matrices. The generalized subspace is then decomposed into signal subspace and noise subspace. Enhancement is performed by nulling the components in the noise subspace and retaining the components in the signal subspace. The performance of the proposed algorithm is tested with simulated and real data, and compared with recently proposed signal subspace techniques. With the simulated data, the algorithms are used to estimate the latencies of P100, P200, and P300 of VEP signals corrupted by additive colored noise at different values of SNR. With the real data, the VEP signals are collected at Selayang Hospital in Kuala Lumpur, Malaysia, and the capability of the proposed algorithm in detecting the latency of P100 is obtained and compared with other subspace techniques. The ensemble averaging technique is used as a baseline for this comparison. The results indicated significant improvement by the proposed technique in terms of better accuracy and less failure rate.

Item Type:Article
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Departments / MOR / COE:Research Institutes > Institute for Health Analytics
ID Code:5050
Deposited By: Assoc Prof Dr Nidal Kamel
Deposited On:23 Mar 2011 06:49
Last Modified:19 Jan 2017 08:23

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